Non-Linear Pattern Recognition based on SVM and Genetic Algorithm

摘要：

This paper presents a support vector machine (SVM) model structure, the genetic algorithm parameters of the model portfolio optimization model, and used for non-linear pattern recognition. The method is not only effective for linear problems, nonlinear problems application and simple and easy, but also proves better than the multi-segment linear classifier design methods and BP network algorithm that returns with errors. Examples show the efficiency of 100％ recognition.

关键词： genetic algorithm support vector machine pattern recognition nonlinear combinatorial optimization.

作者: Wang Jingfang

作者单位: Dept. of Electric Engineering, Hunan International Economics University, Changsha, China

会议类型: 国际会议

会议名称: 2011 International Conference on Image Analysis and Signal Processing(2011第三届图像分析与信号处理国际会议 IASP 2011)

会议地点: 武汉

会议语种:英文

页码: 694-698

在线出版日期: 2011-10-21（万方平台首次上网日期，不代表论文的发表时间）